Evaluating high-resolution mean radiant temperature within an urban street canopy: Resolving spatiotemporal variations with LiDAR/thermal infrared scanning and data-driven simulation
Bibliographic record
Abstract
As extreme weather intensifies, the increasing frequency of summer heatwaves make the study of human thermal exposure essential. Mean radiant temperature (Tmrt) is crucial for assessing human thermal exposure, because it quantifies the largest sources of spatial variability in pedestrian-perceived thermal stress and comfort in complicated urban environments. Despite the availability of existing methods for evaluating Tmrt through measurements and numerical simulations, the lack of detailed urban three-dimensional (3D) models and spatially and temporally resolved pedestrian-level irradiance from urban surfaces poses a significant challenge in obtaining high-resolution Tmrt data. This paper introduces a methodology that combines LiDAR and thermal infrared scanning with data-driven simulations. The approach was applied to a street canyon segment in Salt Lake City during the summer, to enable assessment of high spatial resolution (0.3 m2) shortwave and longwave radiant fluxes of urban surfaces at different periods. Based on the refined 3D radiation field, different irradiance sources received by the human body at different locations was sampled and then a high-spatial-resolution field (0.5 m2) of pedestrian-level sampled Tmrt (Tmrt_sampled) was generated. Such a method for calculating Tmrt_sampled is efficient, requiring only 30 seconds of computational time for each simulated instant. Results indicated significant variations of across heterogeneous urban spaces, with the largest difference exceeding 35 ℃. Spatiotemporal variations in longwave irradiance from urban surfaces significantly influenced Tmrt_sampled. Exposure of ground and wall materials to direct sunlight, coupled with their substantial thermal inertia, drove peak human thermal stress by 17:00. Furthermore, Tmrt_sampled was compared with SOLWEIG-simulated (Tmrt_simulated ) for the same meteorological conditions. Due to differences in mesh and mechanism for quantifying Tmrt, Tmrt_sampled values typically were 4 ~ 6 ℃ higher than Tmrt_simulated over sunlit surfaces, and their root mean square error reached 4.71 ℃ when the solar elevation was high and ground shadows were minimal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".